---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: caption
    list: string
  - name: sentids
    list: string
  - name: split
    dtype: string
  - name: img_id
    dtype: string
  - name: filename
    dtype: string
  splits:
  - name: train
    num_bytes: 4044387988
    num_examples: 29000
  - name: test
    num_bytes: 142155397
    num_examples: 1000
  - name: validation
    num_bytes: 140557396.192
    num_examples: 1014
  download_size: 4306311970
  dataset_size: 4327100781.192
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
task_categories:
- text-generation
- image-to-text
- text-to-image
language:
- pt
pretty_name: Flickr30K Portuguese Translated
size_categories:
- 10K<n<100K
---

# 🎉 Flickr30K Translated for Portuguese Image Captioning

## 💾 Dataset Summary

Flickr30K Portuguese Translated, a multimodal dataset for Portuguese image captioning with 31,014 images, each accompanied by five descriptive captions that have been
generated by human annotators for every individual image. The original English captions were rendered into Portuguese
through the utilization of the Google Translator API.

The dataset is one of the results of work available at: https://github.com/laicsiifes/ved-transformer-caption-ptbr.

## 🧑‍💻 Hot to Get Started with the Dataset

```python
from datasets import load_dataset

dataset = load_dataset('laicsiifes/flickr30k-pt-br')
```

## ✍️ Languages

The images descriptions in the dataset are in Portuguese.

## 🧱 Dataset Structure

### 📝 Data Instances

An example looks like below: 

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333>,
  'caption':[
    'Um cachorro preto carrega um brinquedo verde na boca enquanto caminha pela grama.',
    'Um cachorro preto molhado carrega um brinquedo verde pela grama.',
    'Um cachorro preto carregando algo pela grama.',
    'Um cachorro na grama com um item azul na boca.',
    'Um cachorro preto tem um brinquedo azul na boca.'
  ],
  'sentids': ['450', '451', '452', '453', '454'],
  'split': 'train',
  'img_id': '90',
  'filename': '1026685415.jpg'
}
```

### 🗃️ Data Fields

The data instances have the following fields:

- `image`: a `PIL.Image.Image` object containing image.
- `caption`: a `list` of `str` containing 5 captions related to image.
- `sentids`: a `list` of `str` containing 5 ordered identification numbers related to each caption.
- `split`: a `str` containing data split. It stores texts: `train`, `val` or `test`.
- `img_id`: a `str` containing image identification number.
- `filename`: a `str` containing name of image file.

### ✂️ Data Splits

The dataset is partitioned using the Karpathy splitting appoach for Image Captioning
([Karpathy and Fei-Fei, 2015](https://arxiv.org/pdf/1412.2306)).

|Split|Samples|Average Caption Length (Words)|
|:-----------:|:-----:|:--------:|
|Train|29,000|12.1 ± 5.1|
|Validation|1,014|12.3 ± 5.3|
|Test|1,000|12.2 ± 5.4|
|Total|31,014|12.1 ± 5.2|


## 📋 BibTeX entry and citation info

```bibtex
@inproceedings{bromonschenkel2024comparative,
  title={A Comparative Evaluation of Transformer-Based Vision Encoder-Decoder Models for Brazilian Portuguese Image Captioning},
  author={Bromonschenkel, Gabriel and Oliveira, Hil{\'a}rio and Paix{\~a}o, Thiago M},
  booktitle={2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},
  pages={1--6},
  year={2024},
  organization={IEEE}
}
```